Mutagenic and carcinogenic structural alerts and their mechanisms of action

Plošnik, Alja; Vračko, Marjan; Dolenc, Marija Sollner

doi:10.1515/aiht-2016-67-2801

Cited by 39 publications

(33 citation statements)

References 60 publications

(66 reference statements)

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“…Thus, although CBZ showed dose proportionality, CBN had obvious nonlinear elimination pharmacokinetics with greater than dose‐proportional increases in exposure, which is probably due to saturable sequential metabolism or efflux of CBN. Since CBN contains a structural alert associated with mutagenicity (Benigni et al, ; Plošnik et al, ), the enhanced exposure of CBN may have potential safety concerns.…”

Section: Resultsmentioning

confidence: 99%

“…Since CBN contains a structural alert associated with mutagenicity (Benigni et al, 2010;Plošnik et al, 2016), the enhanced exposure of CBN may have potential safety concerns.…”

Section: Application To a Pharmacokinetic Studymentioning

confidence: 99%

“…Moreover, changes in CYP3A4 activity resulting from the co‐administration of CYP3A4 inhibitors or inducers may cause clinically significant drug–drug interactions (Nguyen et al, ). CBN contains an aromatic ring N‐ oxide (Figure ) which is a mutagenic and carcinogenic structural alert (Benigni, Bossa, & Worth, ; Plošnik, Vračko, & Dolenc, ). Thus, CBZ may have potential safety concerns when the activity of CYP3A4 is enhanced by numerous inducers like rifampicin, carbamazepin or efavirenz, causing higher exposure of CBN, and there is a growing demand for the simultaneous quantification of CBZ and CBN for the pharmacokinetic studies.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, changes in CYP3A4 activity resulting from the co-administration of CYP3A4 inhibitors or inducers may cause clinically significant drug-drug interactions . CBN contains an aromatic ring N-oxide ( Figure 1) which is a mutagenic and carcinogenic structural alert (Benigni, Bossa, & Worth, 2010;Plošnik, Vračko, & Dolenc, 2016).…”

mentioning

confidence: 99%

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A sensitive LC–MS/MS method for simultaneous determination of cabozantinib and its metabolite cabozantinib N‐oxide in rat plasma and its application in a pharmacokinetic study

Ren

Sun

et al. 2018

Biomedical Chromatography

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Cabozantinib (CBZ) is used for the treatment of progressive, metastatic medullary thyroid cancer. Its major oxidative metabolite is cabozantinib N-oxide (CBN), which contains a structural alert associated with mutagenicity, yet the pharmacokinetics studies lack the simultaneous investigation of CBN and dose proportionality. In the current study a simple LC-MS/MS method was developed and validated for the simultaneous estimation and pharmacokinetic investigation of CBZ and CBN in rat plasma. The analytes were separated on a Waters Atlantics C column (2.1 × 150 mm, 3 μm). The mass spectrometry analysis was conducted in positive ionization mode with multiple reaction monitoring. Good linearity was observed over the concentration ranges of 0.500-5000 ng/mL for CBZ and 0.525-2100 ng/mL for CBN. The extraction recoveries were constant and the intra- and inter-batch precision and accuracy were acceptable for the analysis of biological samples. The method was successfully applied for the simultaneous estimation of CBZ and CBN in a pharmacokinetic study in Sprague-Dawley rats. After oral administration of CBZ (1, 5 and 12.6 mg/kg), although CBZ showed dose proportionality, the metabolite CBN showed obvious nonlinear elimination pharmacokinetics with greater than dose-proportional increases in exposure.

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Section: Resultsmentioning

confidence: 99%

“…Since CBN contains a structural alert associated with mutagenicity (Benigni et al, 2010;Plošnik et al, 2016), the enhanced exposure of CBN may have potential safety concerns.…”

Section: Application To a Pharmacokinetic Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A sensitive LC–MS/MS method for simultaneous determination of cabozantinib and its metabolite cabozantinib N‐oxide in rat plasma and its application in a pharmacokinetic study

Ren

Sun

et al. 2018

Biomedical Chromatography

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“…The SAs are molecular substructures or reactive groups that are related to the toxicity of chemicals, and represent a sort of 'codification' of a long series of studies aimed at highlighting the mechanisms of action of the toxic endpoints (Plosnik, Vracko, & Dolenc, 2016). In this study, the SMARTS with high frequencies in positive reproductive toxicity compounds were obtained by SARPy, the minimum atoms number for Structural Alerts Options was set as 2 and the maximum atoms number was set as 15, other parameters were set by default in SARPy, and then the information gain (Shen, Cheng, Xu, Li, & Tang, 2010) and frequency analysis (B. F. Jensen, Vind, Padkjaer, Brockhoff, & Refsgaard, 2007) methods were used to analyze the reproductive toxicity SAs.…”

Section: Analysis Of Structural Alertsmentioning

confidence: 99%

In silico prediction of chemical reproductive toxicity using machine learning

Jiang

Yang

et al. 2019

J of Applied Toxicology

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Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strategies have been developed to assess the potential reproductive toxicity (reproductive toxicity) of chemicals. Some prediction models for reproductive toxicity have been developed, but most of them were built only based on one single endpoint such as embryo teratogenicity; therefore, these models may not provide reliable predictions for toxic chemicals with other endpoints, such as sperm reduction or gonadal dysgenesis. Here, a total of 1823 chemicals for reproductive toxicity characterized by multiple endpoints were used to develop structure‐activity relationship models by six machine‐learning approaches with nine molecular fingerprints. Among the models, MACCSFP‐SVM model has the best performance for the external validation set (area under the curve = 0.900, classification accuracy = 0.836). The applicability domain was analyzed, and a rational boundary was found to distinguish inaccurate predictions and accurate predictions. Moreover, several structural alerts for characterizing reproductive toxicity were identified using the information gain combining substructure frequency analysis. Our results would be helpful for the prediction of the reproductive toxicity of chemicals.

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Interpretable Deep Learning in Drug Discovery

Preuer

Klambauer

Rippmann

et al. 2019

Lecture Notes in Computer Science

103

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Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes. We will unravel these black boxes and will demonstrate approaches to understand the learned representations which are hidden inside these models. We show how single neurons can be interpreted as classifiers which determine the presence or absence of pharmacophore-or toxicophore-like structures, thereby generating new insights and relevant knowledge for chemistry, pharmacology and biochemistry. We further discuss how these novel pharmacophores/toxicophores can be determined from the network by identifying the most relevant components of a compound for the prediction of the network. Additionally, we propose a method which can be used to extract new pharmacophores from a model and will show that these extracted structures are consistent with literature findings. We envision that having access to such interpretable knowledge is a crucial aid in the development and design of new pharmaceutically active molecules, and helps to investigate and understand failures and successes of current methods.

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Mutagenic and carcinogenic structural alerts and their mechanisms of action

Cited by 39 publications

References 60 publications

A sensitive LC–MS/MS method for simultaneous determination of cabozantinib and its metabolite cabozantinib N‐oxide in rat plasma and its application in a pharmacokinetic study

A sensitive LC–MS/MS method for simultaneous determination of cabozantinib and its metabolite cabozantinib N‐oxide in rat plasma and its application in a pharmacokinetic study

In silico prediction of chemical reproductive toxicity using machine learning

Interpretable Deep Learning in Drug Discovery

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